Soft Computing
Soft Computing
Soft Computing is a revolutionary component of computer science and is a type of algorithm that produces approximate solutions to unsolvable high-level problems. Typically, traditional hard-computing algorithms heavily rely on concrete data and mathematical models in order to produce solutions to problems. Soft computing emerged in the late 20th century and handles incomplete, obscure problems that handle data and variability. During this time period, revolutionary research in three fields greatly impacted soft computing. Fuzzy logic which is a computational paradigm that allows uncertainties in data to be entertained by using levels of truth rather than rigid 0's and 1's in binary. Next, neural networks which are computational models influenced by human brain functions. Finally, evolutionary computation which are algorithms that mimics natural process such as evolution.
In the context of artificial intelligence and machine learning, soft computing provides tools to handle real-world uncertainties. Its methods supplement preexisting methods for better solutions. Today, the combination with artificial intelligence has led to hybrid-intelligence systems that merge various computational algorithms. Expanding the applications of artificial intelligence, soft computing leads to powerful solutions. Key points include: tackling ambiguity, flexible learning, grasping intricate data, real-world applications, and ethical artificial intelligence. [1]
History
The development of soft computing dates back to the late 20th century. In 1965, Lotfi Zadeh introduces fuzz logic that laid the mathematical groundwork for soft computing. Between the 1960's and 70's, evolutionary computation, the development of genetic algorithms that mimicked biological processes, began to emerge. These models carved the path for models to start handling uncertainty. Although neural network research began in the 40's and 50's, there was a new demand for neural network research in the 80's. Researchers invested time to develop models for pattern recognition. Between the 80's and 90's, existing models began to merge with genetic algorithms, fuzzy logic, and neural networks in order to create hybrid-intelligence systems that were better equipped to solve complicated problems. From the 90's to the present day, Models have been instrumental and affect multiple fields handling big data including: engineering, medicine, social sciences, and finance. [2]
Key Components of Soft Computing
Fuzzy Logic
Fuzzy Logic is an aspect of computing that handles approximate reasoning. Typically, binary logic solely allows computers to make decisions on true or false reasons (0's and 1's); however, the introduction of fuzzy logic allows systems to handle the unknowns between 0 and 1.
Unlike classical sets that allow members to either be completely within the set or out, fuzzy sets allow partial membership by incorporating "graduation" between sets. Fuzzy logic operations include negation, conjunction, and disjunction, which allow for the handling of membership between sets of data.
Fuzzy rules are logical statements that map the correlation between input and output parameters. They set the rules needed to linguistically trace variable relationships. Would not be possible without linguistic variables. Representing values that are typically not quantifiable, linguistic variables allow uncertainties to be entertained.[1][3]
Neural Networks
Evolutionary Computation
Hybrid Intelligence Systems
Applications
Challenges and Limitations
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References
- ^ a b Ibrahim, Dogan. "An overview of soft computing." Procedia Computer Science 102 (2016): 34-38.
- ^ Chaturvedi, Devendra K. "Soft computing." Studies in Computational intelligence 103 (2008): 509-612.
- ^ Trillas, Enric, and Luka Eciolaza. "Fuzzy logic." Springer International Publishing. DOI 10 (2015): 978-3.
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